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    Coursera
    1. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    2. Week 2
    3. Optimization Algorithms
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        Quiz: Optimization Algorithms
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    Optimization Algorithms

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    1.
    Question 1

    Which notation would you use to denote the 3rd layer’s activations when the input is the 7th example from the 8th minibatch?

    1 / 1 point
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    2.
    Question 2

    Which of these statements about mini-batch gradient descent do you agree with?

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    3.
    Question 3

    Why is the best mini-batch size usually not 1 and not m, but instead something in-between?

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    4.
    Question 4

    Suppose your learning algorithm’s cost JJJ, plotted as a function of the number of iterations, looks like this:

    Which of the following do you agree with?

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    5.
    Question 5

    Suppose the temperature in Casablanca over the first three days of January are the same:

    Jan 1st: θ1=10oC\theta_1 = 10^o Cθ1​=10oC

    Jan 2nd: θ210oC\theta_2 10^o Cθ2​10oC

    (We used Fahrenheit in lecture, so will use Celsius here in honor of the metric world.)

    Say you use an exponentially weighted average with β=0.5\beta = 0.5β=0.5 to track the temperature: v0=0v_0 = 0v0​=0, vt=βvt−1+(1−β)θtv_t = \beta v_{t-1} +(1-\beta)\theta_tvt​=βvt−1​+(1−β)θt​. If v2v_2v2​ is the value computed after day 2 without bias correction, and v2correctedv_2^{corrected}v2corrected​ is the value you compute with bias correction. What are these values? (You might be able to do this without a calculator, but you don't actually need one. Remember what bias correction is doing.)

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    6.
    Question 6

    Which of these is NOT a good learning rate decay scheme? Here, t is the epoch number.

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    7.
    Question 7

    You use an exponentially weighted average on the London temperature dataset. You use the following to track the temperature: vt=βvt−1+(1−β)θtv_{t} = \beta v_{t-1} + (1-\beta)\theta_tvt​=βvt−1​+(1−β)θt​. The red line below was computed using β=0.9\beta = 0.9β=0.9. What would happen to your red curve as you vary β\betaβ? (Check the two that apply)

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    True, remember that the red line corresponds to β=0.9\beta = 0.9β=0.9. In lecture we had a green line $$\beta = 0.98) that is slightly shifted to the right.

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    True, remember that the red line corresponds to β=0.9\beta = 0.9β=0.9. In lecture we had a yellow line $$\beta = 0.98 that had a lot of oscillations.

    8.
    Question 8

    Consider this figure:

    These plots were generated with gradient descent; with gradient descent with momentum (β\betaβ = 0.5) and gradient descent with momentum (β\betaβ = 0.9). Which curve corresponds to which algorithm?

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    9.
    Question 9

    Suppose batch gradient descent in a deep network is taking excessively long to find a value of the parameters that achieves a small value for the cost function J(W[1],b[1],...,W[L],b[L])\mathcal{J}(W^{[1]},b^{[1]},..., W^{[L]},b^{[L]})J(W[1],b[1],...,W[L],b[L]). Which of the following techniques could help find parameter values that attain a small value forJ\mathcal{J}J? (Check all that apply)

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    10.
    Question 10

    Which of the following statements about Adam is False?

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